We first compare five state-of-the-art few-shot learning methods, including i) MatchingNet [1]}, ii) ProtoNet [2]}, iii) TransPropNet [3]}, iv) FTNET [4]},
and v) Transported Prototypes (TP) [5]}. As shown in Table REF , PGADA outperforms the best baseline (TP) by at least \(13.12\%\)  in CIFAR100, \(12.51\%\)  in miniImageNet, and \(4.65\%\)  in FEMNIST, respectively. Note that PGADA achieves the best improvement on miniImageNet, since the image size of miniImageNet is the largest one compared to CIFAR100 and FEMNIST. In addition, PGADA achieves better improvement in the tasks of 1-shot (i.e., \(22.46\%\) ) than the tasks of 5-shot (\(14.77\%\) ) averagely, which demonstrates the robustness of our method, especially with limited labeled data. Compared with the first four baselines, i.e., ProtoNet, MatchingNet, TransPropNet, and FTNET, our method achieves consistent improvement, \(41.48\%\) , \(39.78\%\) , \(71.61\%\) , and \(54.85\%\) , respectively, since these methods do not consider the inherent distribution shift between the support and query set. While TP also utilizes optimal transport to align the support and query set, it still shows relatively weak performance compared to PGADA because the transportation plan of TP is misguided by the small perturbations in the images, as proved in Theorem REF . Since PGADA is a model agnostic adversarial alignment framework, we equip PGADA with different classifiers, e.g., ProtoNet and MatchingNet. Our framework outperforms the baseline by \(39.78\%\)  and \(21.81\%\)  in ProtoNet and MatchingNet, respectively, manifesting the generability of PGADA. We observe that ProtoNet outperforms MatchingNet in the 5-shot case as the prototypes reduce the bias by averaging the embedding vectors, which is more robust.
